LLMs can perform complex tasks like drafting contracts or answering medical questions, but without safeguards, they pose serious risks—like leaking PII, giving unauthorized advice, or enabling fraud. NVIDIA’s NeMo Guardrails provides a modular safety framework that enforces AI safety through configurable input and output guardrails, covering risks such as PII exposure, jailbreaks, legal liability, and regulatory violations. In high-stakes areas like healthcare, it blocks unauthorized diagnoses and ensures HIPAA/FDA compliance. Each blocked action includes explainable metadata for auditing and transparency, turning AI safety from a black-box filter into configurable, measurable infrastructure.

As AI agents become more capable, their real-world performance increasingly depends on how well they can coordinate tools. This month's paper introduces a benchmark designed to rigorously test how AI agents handle multi-step tasks using the Model Context Protocol (MCP) — the emerging standard for tool integration. ​The authors present 101 carefully curated real-world queries, refined through iterative LLM rewriting and human review, that challenge models to coordinate multiple tools such as web search, file operations, mathematical reasoning, and data analysis.